Recommend a cool geospatial data visualization tool with low code

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In the open source geospatial information data visualization tool , Most of them need a certain programming foundation and skills to use . And today I want to introduce this tool , You don't need any programming foundation , Visualization of geospatial data can be realized , And the effect is very cool .

This one is called kepler.gl Open source tools for , Is based on Uber Big data visualization open source project deck.gl Created , Depending on WebGL Powerful graphics rendering capabilities , Large scale data sets can be easily displayed in various forms on the browser side .

adopt kepler.gl oriented Python Open interface package keplergl, We can do it in Jupyter Notebook/Lab By writing simple Python The way code passes in data of various formats , Embedded in it Notebook/Lab In the interactive window, it uses many kinds of rich spatial data visualization functions built in it . You can also directly use the demo Tools , Configure the visualization directly after importing data .

/ Use on the official website /

This method of use does not require writing any code , Visualization of geospatial data can be realized ！

Visit the following official website address directly ：

https://kepler.gl/demo

Upload data files （ Supported formats are CSV, Json, GeoJSON） After that, the visualization configuration and output of geospatial data can be carried out online （ The configuration operation of each function will be introduced in detail below ）.

/ stay Jupyter Use in /

This method needs to be in anaconda Install in Python Of keplergl Module library , And pass Jupyter Notebook/Lab The interactive window of .

1

Prepare before use

It needs to be installed before use keplergl Of Python Library and its related dependent packages .

1.1

install keplergl modular

stay Anaconda Prompt Enter the following command in the tool to install keplergl modular , Relevant dependent packages will be automatically downloaded and installed ：

conda install -c conda-forge keplergl

Because there are many dependent packages , The installation needs to wait for some time .

1.2

install jupyter labextension

If you use JupyterLab, You also need to install an extension , stay Anaconda Prompt Enter the following command in the tool to install ：

jupyter labextension install @jupyter-widgets/jupyterlab-manager keplergl-jupyter

After installation , restart Anaconda, And on again JupyterLab You can use .

2

Initialize the map form

Instantiate a KeplerGl() object , View the initialized map object operation window .

【 Parameter Introduction 】

KeplerGl() Parameters of ：

• height：int type , The height of the displayed map form , The default is 400px
• data：dict type , Dictionary data set in the incoming map
• config：dict type , Map parameter configuration information
• show_docs：bool type , Set whether to display the address of the user guidance document , The default is True, It shows

【 Code 】

from keplergl import KeplerGl

#  Load the initialization map form 
map_a = KeplerGl(height=800, show_docs=False)
map_a

【 result 】

appear kepler.gl Initialize the map operation window , Click the small arrow in the upper left corner to expand a feature rich configuration panel ：

chart 2-1：kepler.gl Initialize the map form

This function panel is used for visual configuration , It will be introduced in detail below .

3

Load geographic data file

kepler.gl Support 4 Data formats , Namely ：CSV, GeoJSON, DataFrame, GeoDataFrame. Some geographic data visualization types only support GeoJSON Formatted data .

kepler.gl Would be right csv Infer the field type of the formatted file , Therefore, you must add the corresponding lat、lng part , There are three formats ：

• <name>_lat, <name>_lng
• <name>_lat, <name>_lon
• <name>_latitude, <name>_longitude

among <name> Is the optional variable name . If you do not name the longitude and latitude field name in this format , The imported data cannot be automatically recognized as possible graphic objects , Manual selection is required .

We use 《 After the eruption of Tonga volcano , Analyze the global volcanic distribution , The most volcanoes are found here ！》 Volcano data set downloaded from csv file , adopt Pandas The module reads the data and stores it DataFrame in , And then use it keplergl Library add_data() Method to load the data information into the map layer .

【 Parameter Introduction 】

add_data() Parameters of ：

• data： Data set in the incoming map , It can be CSV, GeoJSON, DataFrame
• name： The name displayed in the incoming layer of the dataset

【 Code 】

import pandas as pd

#  Read in the data file 
df = pd.read_csv("volcano_keplergl.csv",encoding='ANSI')

#  Create a new map form , And load geographic data 
map_b = KeplerGl(height=800, show_docs=False)
map_b.add_data(data=df, name="volcano")

map_b

【 result 】

chart 3-1： Load geographic data information to kepler.gl In the map

After the geographic data information is loaded into the map , By default, the most appropriate data layer visualization type will be selected to display its geographical location .

4

Geospatial data visualization configuration

And pyecharts, matplotlib, folium And other visualization libraries are configured in different ways , stay kepler.gl There is no need to write colors 、 Icon 、 style 、 Trigger event and other parameter configuration codes , You only need to perform visual configuration operations in the function panel on the page , The configured parameters can be reused , Really realize the use of low code .

The following describes the configuration operations of some common functions .

4.1

Layers（ Layers ） function

Data display layer , Above the underlying map layer . Used to set after importing data , The style of data layer display . Multiple sets of data can be displayed by layer superposition , You can also create new layers by yourself .

There are as many data layers 40+ Two color schemes are available （ Each color scheme will be based on the selected steps Adjust the quantity accordingly ）, You can also customize the configuration ; You can set the reference field of color matching （ I'm going to set it to region）、 transparency ：

chart 4-1-1： Set up kepler.gl Data layer color scheme

You can configure the type of visualization for each data layer , Different types can set different contents . For example, for punctuation type, you can set the radius of the marked point 、 Outline, line color, etc ; Path type can set the thickness of path lines 、 Altitude ratio, etc ：

chart 4-1-2： Set up kepler.gl Data layer visualization type

Arc and Line Path visualization can display path information on the map , The premise is that there are two groups of longitude and latitude coordinate data , That is, the starting point coordinate and the ending point coordinate ：

chart 4-1-3：kepler.gl The path information is displayed on the map

Some visualization types （ Such as Hexbin, Cluster） You can also pass the height （ At an altitude of ） data , Draw out 3D Data map ：

chart 4-1-4：kepler.gl draw 3D Data map example 1

chart 4-1-5：kepler.gl draw 3D Example 2 of data map

chart 4-1-6：kepler.gl draw 3D Example 3 of data map

chart 4-1-7：kepler.gl draw 3D Example 4 of data map

【 actual combat 】

Add the second group of longitude and latitude data to the original volcanic data set , Realize the function of displaying path information .

chart 4-1-8：kepler.gl Display path information actual battle

Because the added second group of endpoint coordinate data is randomly generated , So the path is messy 、 No rules , You can use more real data to realize path display .

4.2

Filters （ filter ） function

To filter the data in the layer .

Numerical data , The range of values can be selected ; String data , You can filter by selecting the corresponding value ; Time data , You can add a timeline , Cooperate with path information , It can also realize the animation of time rotation path .

chart 4-2-1：kepler.gl Layer data filtering

chart 4-2-2：kepler.gl Realize the animation of time rotation path

【 actual combat 】

On the basis of adding the second group of longitude and latitude data to the original volcanic data set , Then add the time information , Pass time filter condition , Realize the animation function of time rotation path .

chart 4-2-3：kepler.gl Add time filtering conditions to the data layer

chart 4-2-4：kepler.gl Realize the Animation Practice of time rotation path

After adding the filter conditions of the time type field in the data layer , The layer will automatically generate the rotation timeline , Click the play button to generate path information in chronological order for rotation . It can be applied to travel 、 logistics 、 Routes and other big data display .

4.3

Interaction（ Interaction ） function

Set the interactive data information that triggers the event .

You can set the data information content displayed on the target , Set whether to display longitude and latitude coordinate information , Set whether to allow location search through geographic encoder .

chart 4-3-1： Set up kepler.gl Trigger event interaction information

Here, hover is set to display the name of the volcano 、 type 、 The year of the last eruption 、 Country 、 Area and longitude and latitude coordinate information , Allow location search via geocoder .

4.4

Base map（ Base map ） function

Set the style of the underlying map and the layer order and layer content of the underlying map .

There are currently five map styles to choose from , You can also specify external or customized map styles ; You can set the display content and order of map layers . in addition 3D The map can also set the color of buildings .

chart 4-4-1：kepler.gl Bottom map style and layer settings

There are many interesting configurations in each function , You can explore .

5

Get map configuration information

adopt .config You can get all the configuration information in the current map , And json Format returns the result . extracted config Parameters , You can directly pass it into its layer when initializing another new form , To reuse to other geographic data .

【 Code 】

#  Display layer configuration information 
map_b.config

#  Initialize new form , And import data and  map_b  Configuration information 
map_c = KeplerGl(height=800, data={"volcano_new": df}, config=map_b.config, show_docs=False)
map_c

【 result 】

chart 5-1： Output kepler.gl Layer configuration parameter information

chart 5-2： Reuse data and layer configuration information into new layers

Volcanic data sets and map_b The layer configuration parameter information of was successfully imported into the new layer .

6

Export map files for visual configuration

adopt kepler.gl Of .save_to_html() Method , The visualization results of geospatial data can be generated html file , Further configuration and use .

【 Parameter Introduction 】

.save_to_html() Parameters of ：

• data：dict type , Geographic data , If it is blank, the geographic data in the current map will be used
• config：dict type , Map configuration information , If it is empty, the current configuration in the map will be used
• file_name：str type , Exported file name , The default is keplergl_map.html
• read_only：bool type , Set whether the exported file hides the configuration panel , The default is Fasle, That is, it supports configuration

【 Code 】

#  Export geospatial data visualization map file 
map_c.save_to_html(file_name="volcano_keplergl_map.html")

【 result 】

chart 6-1： Derived kepler.gl Map file

Next close Jupyter, Find the exported... In the working directory html Open the file directly , Then you can do it in the browser （ Happy ） The reason is （ Yue ） Count （ huan ） According to the （ fast ） Visual operation of （ play ） do （ Play ） 了 .